System And Method For Large-Scale Data Processing Using An Application-Independent Framework

ABSTRACT

A method performs large-scale data processing in a distributed and parallel processing environment. The method defines application-independent map and reduce operations, each invoking one or more library functions that automatically handle data partitioning, parallelization of computations, and fault tolerance. A user specifies a map operation, which calls one or more of the application-independent map operators to perform data read and write operations. A user also specifies a reduce operation, which calls one or more of the application-independent reduce operators to perform data read and write operations. The method executes application-independent map worker processes. Each map worker process executes the user-specified map operation to read designated portions of input files and store intermediate data values in intermediate data structures. The method also executes application-independent reduce worker processes. Each reduce worker process executes the user-specified reduce operation to read intermediate data values from the intermediate data structures and produce final output data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/417,126, filed May 20, 2019, and will issue as U.S. Pat. No.10,885,012 on Jan. 5, 2021, which is a continuation of U.S. patentapplication Ser. No. 15/479,228, filed Apr. 4, 2017, (now U.S. Pat. No.10,296,500), which is a continuation of U.S. patent application Ser. No.14/099,806, filed Dec. 6, 2013, (now U.S. Pat. No. 9,612,883), which isa continuation of U.S. patent application Ser. No. 12/686,292, filedJan. 12, 2010 (now U.S. Pat. No. 8,612,510), which is a continuation ofU.S. patent application Ser. No. 10/871,245, filed Jun. 18, 2004 (nowU.S. Pat. No. 7,756,919), each of which is incorporated by reference inits entirety.

This application is also related to U.S. patent application Ser. No.10/871,244 filed Jun. 18, 2004, now U.S. Pat. No. 7,650,331, which ishereby incorporated by reference in its entirety.

TECHNICAL FIELD

The disclosed embodiments relate generally to data processing systemsand methods, and in particular to a framework for simplifyinglarge-scale data processing.

BACKGROUND

Large-scale data processing involves extracting data of interest fromraw data in one or more datasets and processing it into a useful dataproduct. The implementation of large-scale data processing in a paralleland distributed processing environment typically includes thedistribution of data and computations among multiple disks andprocessors to make efficient use of aggregate storage space andcomputing power.

Various functional languages (e.g., LISP™) and systems provideapplication programmers with tools for querying and manipulating largedatasets. These conventional languages and systems, however, fail toprovide support for automatically parallelizing these operations acrossmultiple processors in a distributed and parallel processingenvironment. Nor do these languages and systems automatically handlesystem faults (e.g., processor failures) and I/O scheduling.

SUMMARY

A system and method for large-scale data processing includes operationsfor automatically handling programming details associated withparallelization, distribution, and fault-recovery. In some embodiments,application programmers can process large amounts of data by specifyingmap and reduce operations. The map operations retrieve data (e.g.,key/value pairs) from input data files and produce intermediate datavalues in accordance with the mapping operations. The reduce operationsmerge or otherwise combine the intermediate data values in accordancewith the reduce operations (e.g., combining intermediate values thatshare the same key). In some embodiments, the system and methods use amaster process to manage tasks and one or more local databases to reducenetwork traffic and file system (FS) reads.

In some embodiments, a system for large-scale processing of data in aparallel processing environment includes one or more map modulesconfigured to read input data and to apply at least oneapplication-specific map operation to the input data to produceintermediate data values. An intermediate data structure stores theintermediate data values. The system also includes reduce modules, whichare configured to retrieve the intermediate data values from theintermediate data structure and to apply at least one user-specifiedreduce operation to the intermediate data values to provide output data.Preferably, the map and/or reduce operations are automaticallyparallelized across multiple processors in the parallel processingenvironment. The programs or instructions for handling parallelizationof the map and reduce operation are application independent. The inputdata and the intermediate data values can include key/value pairs andthe reduce operation can include combining intermediate data valueshaving the same key. The intermediate data structure can include one ormore intermediate data files coupled to each map module for storingintermediate data values. The map and reduce operations can beimplemented on different processors coupled to a distributed network.The output data can be written to a file system, which is accessible viathe distributed network.

In some embodiments, a system for large-scale processing of data in aparallel processing environment includes a set of interconnectedcomputing systems. At least one of the computing systems includes a setof application independent map modules configured for reading portionsof input files containing data, and for applying at least oneapplication-specific map operation to the data to produce intermediatekey-value pairs. The system also includes a set of applicationindependent reduce modules, which are configured to apply at least oneapplication-specific reduce operation to the intermediate key-valuepairs so as to combine intermediate values sharing the same key. In oneembodiment, the application independent map modules and applicationindependent reduce modules are both incorporated into a same process,sometimes called a worker process.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a large-scale data processing model.

FIG. 2 is a block diagram of a large-scale data processing system.

FIG. 3 is a block diagram of a large-scale data processing system,including a master process for managing tasks.

FIG. 4 is a block diagram of a computer system for the data processingsystems shown in FIGS. 2 and 3.

FIG. 5 is a block diagram of a data distribution network for large-scaledata processing.

FIG. 6 is a flow diagram of an embodiment of a process for assigningtasks to processes.

FIG. 7A is a block diagram of an exemplary task status table.

FIG. 7B is a block diagram of an exemplary process status table.

DESCRIPTION OF EMBODIMENTS Large-Scale Data Processing Model

FIG. 1 is a block diagram of a large-scale data processing model 100.The model 100 generally includes mapping operations 102 and reductionoperations 104. The mapping operations 102 apply one or more mappingoperations to a set of input data a, (e.g., text files, records, logs,sorted maps, etc.) to provide a set of intermediate data values β_(i).The reduction operations 104 apply one or more reduction operations tothe set of intermediate data values β_(i) to provide a set of outputdata φ_(i), (e.g., tables, sorted maps, record I/O, etc.). In someembodiments, the mapping operations 102 are implemented by one or moreapplication-specific mapping functions, which map a set of input data a,to a set of intermediate data values β_(i). The intermediate data valuesβ_(i) are stored in one or more intermediate data structures. Someexamples of intermediate data structures include, without limitation,files, buffers, histograms, count tables and any other suitable datastructure or device for storing digital information. The intermediatedata values β_(i) are processed by the reduction operations 104, whichare implemented by one or more application-specific reduction functions,which reduce the set of intermediate data values β_(i) to a set ofoutput data φ_(i).

Distributed Data Processing System

In order to explain the operation of the large scale data processingsystem, it is helpful to consider an exemplary distributed dataprocessing system in which the large scale data processing is performed.In general, the embodiments described here can be performed by a set ofinterconnected processors that are interconnected by one or morecommunication networks.

FIG. 5 is a block diagram of an exemplary distributed data processingsystem 500. It should be appreciated that the layout of the system 500is merely exemplary and the system 500 may take on any other suitablelayout or configuration. The system 500 is used to store data, performcomputational tasks, and transmit data between datacenters DC1-DC4. Thesystem may include any number of data centers DCx, and thus the numberof data centers shown in FIG. 5 is only exemplary. The system 500 mayinclude dedicated optical links or other dedicated communicationchannels, as well as supporting hardware such as modems, bridges,routers, switches, wireless antennas and towers, and the like. In someembodiments, the network 500 includes one or more wide area networks(WANs) as well as multiple local area networks (LANs). In someembodiments, the system 500 utilizes a private network, i.e., the systemand its interconnections are designed and operated exclusively for aparticular company or customer. Alternatively, a public network may beused.

Some of the datacenters DC1-DC4 may be located geographically close toeach other, and others may be located far from the other datacenters. Insome embodiments, each datacenter includes multiple racks. For example,datacenter 502 (DC1) includes multiple racks 508 a, . . . , 508 n. Theracks 508 can include frames or cabinets into which components aremounted. Each rack can include one or more processors (CPUs) 510. Forexample, the rack 508 a includes CPUs 510 a, . . . , 510 n (slaves 1-16)and the n^(th) rack 506 n includes multiple CPUs 510 (CPUs 17-31). Theprocessors 510 can include data processors, network attached storagedevices, and other computer controlled devices. In some embodiments, atleast one of processors 510 operates as a master processor, and controlsthe scheduling and data distribution tasks performed throughout thenetwork 500. In some embodiments, one or more processors 510 may take onone or more roles, such as a master and/or slave. A rack can includestorage (e.g., one or more network attached disks) that is shared by theone or more processors 510.

In some embodiments, the processors 510 within each rack 508 areinterconnected to one another through a rack switch 506. Furthermore,all racks 508 within each datacenter 502 are also interconnected via adatacenter switch 504. As noted above, the present invention can beimplemented using other arrangements of multiple interconnectedprocessors.

Further details regarding the distributed network 500 of FIG. 5 can befound in U.S. patent application Ser. No. 10/613,626, entitled “Systemand Method For Data Distribution,” filed Jul. 3, 2003, which applicationis incorporated by reference herein in its entirety.

In another embodiment, the processors shown in FIG. 5 are replaced by asingle large-scale multiprocessor. In this embodiment, map and reduceoperations are automatically assigned to processes running on theprocessors of the large-scale multiprocessor.

Large-Scale Data Processing System I

FIG. 2 is a block diagram of a large-scale data processing system 200.The system 200 provides application programmers with anapplication-independent framework for writing data processing softwarethat can run in parallel across multiple different machines on adistributed network. The system 200 is typically a distributed systemhaving multiple processors, possibly including network attached storagenodes, that are interconnected by one or more communication networks.FIG. 2 provides a logical view of a system 200, which in someembodiments may be implemented on a system having the physical structureshown in FIG. 5. In one embodiment, the system 200 operates within asingle data center of the system 500 shown in FIG. 5, while in anotherembodiment, the system 200 operates over two or more data centers of thesystem 500.

As shown in FIG. 2, a set of input files 202 are processed by a firstset of processes 204, herein called map processes, to produce a set ofintermediate data, represented here by files 206. The intermediate data206 is processed by a second set of processes 208, herein called reduceprocesses, to produce output data 210. Generally each “map process” is aprocess configured (or configurable) to perform map functions and toexecute an application-specific map operator. Each “reduce process” is aprocess configured (or configurable) to perform reduce functions and toexecute an application-specific reduce operator. A control orsupervisory process, herein called the work queue master 214, controlsthe set of processing tasks. As described in more detail below, the workqueue master 214 determines how many map tasks to use, how many reducetasks to use, which processes and processors to use to perform thosetasks, where to store the intermediate data and output data, how torespond to any processing failures, and so on.

It should be noted that the work queue master 214 assigns tasks toprocesses, and that multiple processes may be executed by each of theprocessors in the group of processors that are available to do the workassigned by the work queue master 214. In the context of FIG. 5 or anyother multiple processor system, the set of processes controlled by thework queue master 214 may be a subset of the full set of processesexecuted by the system, and furthermore the set of processors availableto do the work assigned by the work queue master 214 may be fewer thanthe full set of processors in the system. Some of the resources of thesystem may be used for other tasks, such as tasks that generate theinput data 202, or that utilize the output data 210. However, in someembodiments, some or all of the tasks that generate the input data 202or utilize the output data 210 may also be controlled or supervised bythe work queue master 214. In addition, in some embodiments processorscan be added or removed from the processing system during the executionof a map-reduce operation. The work queue master 214 keeps track of theprocessors in the system and the available processes executing on thoseprocessors.

Application programmers are provided with a restricted set ofapplication-independent operators for reading input data and generatingoutput data. The operators invoke library functions that automaticallyhandle data partitioning, parallelization of computations, faulttolerance (e.g., recovering from process and machine failures) and I/Oscheduling. In some embodiments, to perform a specific data processingoperation on a set of input files, the only information that must beprovided by an application programmer is: information identifying of theinput file(s), information identifying or specifying the output files toreceive output data, and two application-specific data processingoperators, hereinafter referred to as map( ) and reduce( ). Generally,the map( ) operator specifies how input data is to be processed toproduce intermediate data and the reduce( ) operator specifies how theintermediate data values are to be merged or otherwise combined. Notethat the disclosed embodiments are not limited to any particular type ornumber of operators. Other types of operators (e.g., data filters) canbe provided, as needed, depending upon the system 200 architecture andthe data processing operations required to produce the desired,application-specific results. In some embodiments, the applicationprogrammers provide a partition operator, in addition to the map( ) andreduce( ) operators. The partition( ) operator, specifies how theintermediate data is to be partitioned over a set of intermediate files.

To perform large-scale data processing, a set of input files 202 aresplit into multiple data blocks 0, . . . , N−1 of either a specified orpredefined size (e.g., 64 MB). Alternately, in some embodiments theinput files 202 have a predefined maximum size (e.g., 1 GB), and theindividual files are the data blocks. A data block is a subset of datathat is retrieved during processing. In some embodiments, the datablocks are distributed across multiple storage devices (e.g., magneticor optical disks) in a data distribution network to fully utilize theaggregate storage space and disk bandwidth of the data processingsystem.

Referring to FIGS. 2 and 5, in some embodiments the input data files 202are stored in one or more data centers DC1-DC4. Ideally, the work queuemaster 214 assigns tasks to processors 510 in datacenters where theinput files are stored so as to minimize network traffic wheneverpossible. In some embodiments, the work queue master 214 uses input fileinformation received from a file system to determine the appropriateprocessor or process for executing a task, using a hierarchical decisionprocess. When a process in a processor in a datacenter DC1-DC4 is idle,it requests a task from the work queue master 214. The work queue master214 searches the input file information received from the file system(e.g., FS 446, FIG. 5), for an unprocessed data block on the machineassigned to process the task. If none are available, the work queuemaster 214 searches the file information for an unprocessed data blockon the same rack 508 as the machine assigned to process the task. Ifnone are available, the work queue master 214 searches the fileinformation for an unprocessed data block in the same datacenter as themachine assigned to process the task. If none are available, the workqueue master 214 will search for unprocessed blocks in otherdatacenters.

By using a hierarchical assignment scheme, data blocks can be processedquickly without requiring large volumes of data transfer traffic on thenetwork 500. This in turn allows more tasks to be performed withoutstraining the limits of the network 500.

Task Management

Referring again to FIG. 2, application programmers develop the map( )and/or reduce( ) operators, which are computer programs that processinput data and intermediate, respectively. In some embodiments theseoperators are compiled into binary files 212 suitable for use on aparticular processing platform. The binary files 202 are loaded into awork queue master module 214, which manages jobs submitted by users ofthe system 200. In some embodiments, the work queue master 214 loads (orcauses to be loaded) onto each process to which it allocates a map orreduce task, the library procedures, and the map( ) or reduce( )operator required to perform the task assigned to the process.

The work queue master 214, when it receives a request to process a setof data using a specified set application-specific map( ) reduce( ) and,optionally, partition( ) operators, determines the number of map tasksand reduce tasks to be performed to process the input data. This may bebased on the amount of input data to be processed. For example, a jobmay include 10,000 map tasks and 10 reduce tasks. In some embodiments,the work queue master module generates a task status table havingentries representing all the tasks to be performed, and then beginsassigning those tasks to idle processes. As noted above, tasks may beallocated to idle processes based on a resource allocation scheme (e.g.,priority, round-robin, weighted round-robin, etc.).

Process and Task Status Tracking

FIG. 6 is a flow diagram of an embodiment of a process 600 for assigningtasks to processes. Process 600 parallelizes a data processing task overas many processes as is consistent with the available computingresources. While the process 600 described below includes a number ofsteps that appear to occur in a specific order, it should be apparentthat the process 600 steps are not limited to any particular order, and,moreover, the process 600 can include more or fewer steps, which can beexecuted serially or in parallel (e.g., using parallel processors or amulti-threading environment). Further, it should be noted that the stepsor acts in process 600 are application-independent and are implementedusing modules or instructions that are application-independent. Only theactual map and reduce operators, which produce intermediate data valuesfrom the input data and that produce output data from the intermediatedata values, respectively, are application-specific. Theseapplication-specific operators are invoked by the map and reduce tasksassigned to processes in step 610. By making a clear boundary betweenthe application-independent aspects and application-specific aspects ofperforming a large scale data processing operation, theapplication-independent aspects can be optimized, thereby making theentire large scale data processing operation very efficient.

The process 600 begins by determining if there are tasks waiting to beassigned to a process (step 606). If there are no tasks waiting, thenthe process 600 waits for all the tasks to complete (step 604). If thereare tasks waiting, then the process 600 determines if there are any idleprocesses (step 608). If there are idle processes, then the process 600assigns a waiting task to an idle process (step 610) and returns to step606. If there are no idle processes, the process 600 waits for an idleprocess (step 614). Whenever a process completes a task, the processsends a corresponding message to the work queue master 214, whichupdates the process and task status tables (step 612). The work queuemaster 214 may then assign a new task to the idle process, if it has anyunassigned tasks waiting for processing resources. For reduce tasks, thework queue master 214 may defer assigning any particular reduce task toan idle process until such time that the intermediate data to beprocessed by the reduce task has, in fact, been generated by the maptasks. Some reduce tasks may be started long before the last of the maptasks are started if the intermediate data to be processed by thosereduce tasks is ready for reduce processing.

In some embodiments, whenever a process fails, which may be discoveredby the work queue master 214 using any of a number of known techniques,the work queue master 214 (A) determines what task was running in thefailed process, if any, (B) assigns that task to a new process, waitingif necessary until an idle process becomes available, and (C) updatesits process and task status tables accordingly. In some embodiments, thework queue master 214 may undertake remedial measures (step 602), suchas causing the failed process to be restarted or replaced by a newprocess. In some embodiments, the work queue master may further detectwhen such remedial measures fail and then update its process statustable to indicate such failures. In addition, in some embodiments, whena map task fails and is restarted in a new process, all processesexecuting reduce tasks are notified of the re-execution so that anyreduce task that has not already read the data produced by the failedprocess will read the data produced by the new process.

FIG. 7A shows an exemplary task status table for keeping track of thestatus of map and reduce tasks. In some embodiments, each task (e.g.,map, reduce) is assigned task ID, a status, a process, and one or moreinput files and output files. In some embodiments, the input files fieldmay specify a portion of an input file (e.g., where the portioncomprises a data block) to be processed by the task, or this field mayspecify portions of two of more input files. The status field indicatesthe current status of the task (e.g., waiting, in-progress, completed,or failed), which is being performed by the assigned process identifiedin the process field. The process retrieves data from one or more inputfiles (or the one or more input file portions) identified in the inputfile field and writes the results of the task ID to one or more outputfiles identified in the output file field. For example, in FIG. 7A, taskRed0000 is assigned to process P0033, which is still in progress. Theprocess P0033 retrieves data blocks from input file 12340 (e.g.,intermediate file A, FIG. 2) and writes the results of the task tooutput file 14000. In some embodiments, until a task is assigned to aprocess, the process field in the task status table indicates that noprocess has yet been assigned to perform that task. It should beapparent that there could be more or fewer fields in the task statustable than shown in FIG. 7A, such as multiple fields for identifyingoutput and input files.

FIG. 7B shows a process status table for keeping track of the status ofall the processes to which the work queue master 214 can assign tasks.In some embodiments, each process is assigned to a task and a location.In some embodiments, each process is permanently assigned to aparticular location (i.e., a particular processor). The status fieldindicates the current status of the process, which performs the assignedtask at the assigned location. For example, process P0010 is “Busy”performing task Map0103 on location CPU011. It should be apparent thatthere could be more or fewer field in the process status table thanshown in FIG. 7B, such as assigning multiple locations assigned to asingle task (e.g., parallel processing).

Map Phase

In some embodiments, the set of application-specific data processingoperations that the map( ) operator can perform is constrained. Forexample, in some embodiments, the map( ) operator may be required toprocess the input data one record at a time, proceeding monotonicallyfrom the first record to the last record in the data block beingprocessed. In some embodiments, the map( ) operator may be required togenerate its output data in the form of key/value pairs. Either the keyor value or both can comprise structured data, as long as the data canbe encoded into a string. For example, the key may have multiple parts,or the value may have multiple parts.

By requiring the map( ) operator's output to be in the form of key/valuepairs, the resulting intermediate data can be mapped to a set ofintermediate data files in accordance with a partition( ) operator. Anexemplary partition( ) operator may specify that all intermediate datais to be directed to an intermediate file corresponding to the value ofthe first byte of the key. Another exemplary partition( ) operator mayspecify that all intermediate data is to be directed to an intermediatefile corresponding to the value of the function “hash(Key) modulo N”,where N is a value specified by the application programmer and“hash(Key)” represents the value produced by applying a hash function tothe key of the key/value pairs in the intermediate data. In someembodiments, the partition operator is always a modulo function and theapplication programmer only specifies the modulus to be used by themodulo function. In one embodiment, the partition operator isautomatically selected by the work queue master 214, or by one of theapplication-independent library functions, discussed below.

In some embodiments, the data blocks 0, . . . , N−1 are automaticallyassigned to map tasks (executed by map processes 204-0, . . . , 204-N−1)in an application independent manner, by the work queue master 214. Inparticular, the work queue master 214 is configured to determine thenumber of data blocks to be processed, and to create a correspondingnumber of instances of the map process 204. Stated in another way, thework queue master 214 assigns a corresponding number of map tasks toprocesses, as suitable processes become available. Since the number ofmap tasks may exceed the number of processes available to the work queuemaster 214, the work queue master 214 will assign as many map tasks asit can to available processes, and will continue to assign the remainingmap tasks to processes as the processes complete previously assignedtasks and become available to take on new tasks. The work queue master214 uses the task status table and process status tables, describedabove, to coordinate its efforts.

Reduce Phase

Application independent reduce modules 208 read intermediate data values(e.g., key/value pairs) from the intermediate files 206. In someembodiments, each reduce module 208 reads from only one intermediatefile 206. The reduce modules 208 sort the intermediate data values,merge or otherwise combine sorted intermediate data values having thesame key and then write the key and combined values to one or moreoutput files 210. In some embodiments, the intermediate file 206 and theoutput files 210 are stored in a File System (FS), which is accessibleto other systems via a distributed network.

Software Implementation

In some embodiments, the map and reduce modules 204 and 208 areimplemented as user-defined objects with methods to carry outapplication-specific processing on data using known object orientedprogramming techniques. For example, a MapReduction base class can becreated that includes methods and data for counting the number of inputfiles that contain a particular term or pattern of terms, sorting theresults of the sort, eliminating duplicates in the sorted results andcounting the number of occurrences of the term. Application programmerscan derive other classes from the base class and instantiate the baseclass as an object in the application code to access its data andmethods.

Large-Scale Data Processing System II

While the system 200 provides good performance for many large-scale dataprocessing, the performance of the system 200 may diminish as the amountof data to be processed and thus the number of tasks increases. Forinstance, performance may be diminished when the size of the data blocksis decreased, thereby increasing the number of map tasks. Since theintermediate files 206 are stored in the FS, an increase in tasksresults in an increase in intermediate file access requests and anassociated increase in network traffic. Additionally, a single workqueue master 214 can only handle a limited number of task assignmentsper time period, beyond which the work queue master 214 begins to limitsystem performance. Increasing the size of those tasks to accommodateadditional jobs could result in load imbalances in the system 200. Theseperformance issues are addressed in the system 300, which is describedbelow with respect to FIG. 3.

FIG. 3 is a block diagram of a large-scale data processing system 300,including a master process 320 (sometimes called a supervisory process)for managing tasks. In system 300, one or more master processes 320assign one or more tasks to one or more worker processes 304 and 308. Insome embodiments, the master process 320 is a task itself (e.g., task 0)initiated by the work queue master module 314 and is responsible forassigning all other tasks (e.g., mapping and reducing tasks) to theworker processes 304, 308, in a master/slave type relationship. Theworker processes 304, 308 include two or more process threads, each ofwhich can be invoked based on the particular task assigned to it by themaster process 320. For example, each worker process 304 invokes a mapthread to handle an assigned map task and invokes a reduce thread tohandle an assigned reduce task. In one embodiment, the worker processes304, 308 include one or more additional threads. For example, a distinctthread may be used to receive remote procedure calls (e.g., from themaster process) and to coordinate work done by the other threads. Inanother example, a distinct thread may be used to handle remote readrequests received from other processors (i.e., peers) in the system.

In one embodiment, the number of worker processes is equal to the numberof machines available in the system 300 (i.e., one worker process permachine). In another embodiment, two or more worker processes are usedin each of the machines in the system 300. If a worker process fails,its task is reassigned to another worker process by the master process320. In some embodiments, the master process 320 or the work queuemaster 314 may undertake remedial measures to repair, restart or replacea failed worker process.

In some embodiments, when the work queue master 314 receives amap/reduce data processing job, the work queue master 314 allocates thejob to a master process 320. The master process 320 determines thenumber (M) of map tasks and the number (R) of reduce tasks to beperformed, and then makes a request to the work queue master 314 for M+Rprocesses (M+R+1, including the master process 320) to be allocated tothe map/reduce data processing job. The work queue master 314 respondsby assigning a number of processes to the job, and sends thatinformation to the master process 320, which will then manage theperformance of the data processing job. If the number of processesrequested exceeds the number of processes available, or otherwiseexceeds the number of processes that the work queue master 314 isallowed to assign to the job, the number of processes assigned to thejob will be less than the number requested.

In some embodiments, all R of the reduce tasks are all immediatelyassigned to processes, but the reduce tasks do not begin work (e.g., ondata sorting) until the master process 320 informs them that there areintermediate files ready for processing. In some embodiments, a singleworker process 304/308 can be assigned both a map task and a reducetask, simultaneously (with each being executed by a distinct processthread), and therefore assigning reduce tasks to processes at thebeginning of the job does not reduce the throughput of the system.

Map Phase

The division of input files 302 into data blocks 0, . . . , N−1, may behandled automatically by the application independent code. Alternately,the user may set an optional flag, or specify a parameter, so as tocontrol the size of the data blocks into which the input files aredivided. Furthermore, the input data may come from sources other thanfiles, such as a database or in-memory data structures.

The input data blocks 0, . . . , N−1, which may in some embodiments betreated as key/value pairs, are read by application independent workerprocesses 304-0, . . . , 304-N−1, as shown in FIG. 3. The input files302 can include a variety of data types typically used in dataprocessing systems, including without limitation text files, record I/O,sorted data structures (such as B-trees), tables and the like. Each ofthe worker processes 304 to which a map task has been assigned appliesthe application-specific map( ) operator to the respective input datablock so as generate intermediate data values. The intermediate datavalues are collected and written to one or more intermediate files 306,which are stored locally at the machine (e.g., in one or more localdatabases) in which the worker process 304 is executed. The intermediatefiles 306 are retained (i.e., they are persistent) until the reducephase completes. Note that in some embodiments, each of the intermediatefiles 306 receives output from only one worker process 304, as shown inFIG. 3. When a worker process 304 completes its assigned task, itinforms the master process 320 of the task status (e.g., complete orerror). If the task was successfully completed, the worker process'sstatus report is treated by the master process 320 as a request foranother task.

In some embodiments, if there are enough worker processes 304 that allthe intermediate values can be held in memory across the workerprocesses, then the system need not write any data to files on localdisks. This optimization reduces execution time for map-reduceoperations in which the number of worker processes is sufficient tohandle all the map tasks at once, and the amount of intermediate data issufficiently small to be kept in memory.

Application-Specific Combiner Function

In some cases, there is significant repetition in the intermediate keysproduced by each map task, and the application-specific Reduce functionis both commutative and associative. When all these conditions apply, aspecial optimization can be used to significantly reduce execution timeof the map-reduce task. An example of a situation in which theoptimization can be applied is a map-reduce operation for counting thenumber of occurrences of each distinct word in a large collection ofdocuments. In this example, the application-specific map function(sometimes called the map( ) operator elsewhere in this document)outputs a key/value pair for every word w in every document in thecollection, where the key/value pair is <w, 1>. The application-specificreduce function (sometimes called the reduce( ) operator elsewhere inthis document) for this example is:

input data is “values”;

int result=0; // initialize result to zero

for each v in values:

-   -   result+=Parselnt(v);

output: <key, result>

Each map task in this example will produce hundreds or thousands ofrecords of the form <word, 1>. The Reduce function simply adds up thecount values. To help conserve network bandwidth for map-reduceoperations that satisfy these properties, the user may provide anapplication-specific Combiner function or operator. The Combinerfunction is invoked with each unique intermediate key and a partial setof intermediate values for the key. This is similar to the Reducefunction, except that it gets executed at the end of each Map task bythe same machine and process that performed by Map task. The Combinerfunction partially summarizes the intermediate key/value pairs. In fact,when using a Combiner function, the same function is typically specifiedfor the Combiner and Reduce operations. The partial combining performedby the Combiner operation significantly speeds up certain classes ofMap-Reduce operations, in part by significantly reducing the amount ofinformation that must be conveyed from the processors that handle Maptasks to processors handling Reduce tasks, and in part by reducing thecomplexity and computation time required by the data sorting and Reducefunction performed by the Reduce tasks.

Reduce Phase

Application independent worker processes 308 which have been assignedreduce tasks read data from the locally stored intermediate files 306.In some embodiments, the master process 320 informs the worker processes308 where to find intermediate data files 306 and schedules readrequests for retrieving intermediate data values from the intermediatedata files 306. In some embodiments, each of the worker processes 308reads a corresponding one of the intermediate files 306 produced by allor a subset of the worker processes 304. For example, consider a systemin which each of the worker processes 304 assigned a map task outputs M(e.g., 100) intermediate files, which we will call Partion-1,j throughPartition-M,j, where j is an index identifying the map task thatproduced the intermediate files. The system will have 100 workerprocesses 308, Worker-1 to Worker-M, each of which reads a correspondingsubset of the intermediate files, Partition-p,j for all valid values of“j,” produced by the worker processes 304, where “p” indicates thepartition assigned to a particular worker process Worker-P (304) and “j”is an index identifying the map tasks that produced the intermediatefiles.

Each worker process 308 sorts the intermediate data values in the subsetof the intermediate files read by that worker process in accordance withthe key of the key/value pairs in the intermediate data. The sorting ofthe key/value pairs is an application independent function of the reducethreads in the worker processes 308. Each worker process 308 also mergesor otherwise combines the sorted intermediate data values having thesame key, and writes the key and combined values to one or more outputfiles 310. The merging or other combining operation performed on thesorted intermediate data is performed by an application-specific reduce() operator. In some embodiments, the output files 310 are stored in aFile System, which is accessible to other systems via a distributednetwork. When a worker process 308 completes its assigned reduce task,it informs the master process 320 of the task status (e.g., complete orerror). If the reduce task was completed successfully, the workerprocess's status report is treated by the master process 320 as arequest for another task. If the reduce task failed, the master process320 reassigns the reduce task to another worker process 308.

Recovering From Task and Processor Failures

In some embodiments, the master process 320 is configured to detect taskand processor failures. When a task failure is detected, the masterprocess 320 reassigns the task to another process. In some embodiments,the master process 320 redistributes the work of the failed task over alarger number of tasks so as to complete that task more quickly than bysimply re-executing the task on another process. The master processsubdivides the work assigned to the failed task to a plurality of newlymini-tasks, and then resumes normal operation by assigning themini-tasks to available processes. The number of mini-tasks may be apredefined number, such as a number between 8 and 32, or it may bedynamically determined based on the number of idle processes availableto the master process. In the case of a failed map task, division of thework assigned to the failed task means assigning smaller data blocks tothe mini-tasks. In the case of a failed reduce task, division of thework assigned to the failed task may mean assigning the data sortingportion of the reduce task to a larger number of worker processes,thereby performing a distributed sort and merge. The resulting sorteddata may, in some embodiments, be divided into a number of files orportions, each of which is then processed using the reduce( ) operatorto produce output data. By detecting such failures and taking theseremedial actions, the amount of delay in completing the entire dataprocessing operation is significantly reduced.

When a processor failure is detected by the master process 320, it maybe necessary to re-execute all the tasks that the failed processorcompleted as well as any tasks that were in process when the processorfailed, because the intermediate results produced by map tasks arestored locally, and the failure of the processor will in many cases makethose results unavailable. Using the status tables, described above, themaster process 320 determines all the tasks that ran on the processor,and also determines which of those tasks need to be re-executed (e.g.,because the results of the tasks are unavailable and are still needed).The master process 320 then updates its status tables to indicate thatthese identified tasks are waiting for assignment to worker tasks.Thereafter, re-execution of the identified tasks is automaticallyhandled using the processes and mechanisms described elsewhere in thisdocument.

In some embodiments, an additional mechanism, herein called backuptasks, is used to guard against task failures as well as task slowdowns. One of the main problems that lengthens the total time taken fora map-reduce operation to complete is the occurrence of “straggler”tasks or machines. A straggler is a process or machine that takes anunusually long time to complete one of the last few map or reduce tasksin the computation. Stragglers can arise for many reasons, includingboth hardware and software errors or conditions. When a large map-reduceoperation is divided into thousands of map and reduce tasks executed bythousands of processes, the risk of a straggler task occurring issignificant. The use of backup tasks, as described next, effectivelyguards against stragglers, without regard to the cause of the problemcausing a process or machine to run slowly. In these embodiments, themaster process determines when the map-reduce operation is close tocompletion. In one embodiment, the criteria for being close tocompletion is that the percentage of map tasks that have completed isabove a threshold. In another embodiment, the criteria for being closeto completion is that the percentage of map and reduce tasks, takentogether, that have completed is above a threshold. The threshold can beany reasonably number, such as 95, 98, or 99 percent, or any percentageabove 90 percent. Once the master process determines that the map-reduceoperation is close to completion, the master process schedules backupexecutions of all remaining tasks. These duplicate tasks may be calledbackup map tasks and backup reduce tasks. FIG. 7A shows an exemplarybackup task, Map103 b, in the task status table. Each task is marked ascompleted when either the primary or backup execution completes. Thismechanism obviously increases the computational resources, and thus insome embodiments the criteria for invoking this mechanism are selectedso as to increase the computational resources by no more than a fewpercent (e.g., five percent). The use of backup tasks significantlyreduces the time to complete large map-reduce operations, often by morethan twenty-five percent.

Master Process & Status Tables

The master process 320 is responsible for assigning tasks to the workerprocesses 304 and 308 and for tracking their status and output.Periodically, the master process 320 solicits a report from each workerprocess assigned a task to determine its task status. In someembodiments, the report can be solicited using a polling scheme (e.g.,round-robin). If the task status indicates that the worker process hasfailed, then the task is put back in the appropriate task queue to bereassigned to another worker process. In some embodiments, the masterprocess 320 maintains status tables 326 for managing tasks, as describedwith respect to FIGS. 7A and 7B.

In one embodiment in which more than one master process 320 is used, alocking mechanism is used to ensure that each of the entries of thestatus tables is modified by only one of the master processes at any onetime. Whenever a master process 320 attempts to assign a map or reducetask to a process, or perform any other management of a map or reducetask, the master process first acquires (or attempts to acquire) a lockon the corresponding status table entry. If the lock is refused, themaster process concludes that the map/reduce task is being managed byanother master process and therefore the master process looks foranother map/reduce task to manage. In another embodiment, the taskstatus table is divided into portions, with each master process beinggiven ownership of a corresponding portion of the task status table, andresponsibility for managing the map/reduce tasks in that portion of thetask status table. Each master process can read other portions of thetask status table, but only uses information in entries indicating thatthe corresponding task has been completed.

The system 300 provides several advantages over other systems andmethods by using one or more master processes to assign and managetasks, together with local databases to store intermediate resultsproduced by the tasks. For example, by distributing file reads overmultiple local databases more machines can be used to complete tasksfaster. Moreover, since smaller tasks are spread across many machines, amachine failure will result in less lost work and a reduction in thelatency introduced by such failure. For example, the FS load for system200 is O(M*R) file opens and the FS load for system 300 is O(M) inputfile opens+O(R) output file opens, where M is the number of map tasksand R is the number of reduce tasks. Thus, the system 200 requiressignificantly more file system file open operations than the system 300.

Computer System for Large-Scale Data Processing

FIG. 4 is a computer system 400 for the data processing systems 200 and300 shown in FIGS. 2 and 3. The computer system 400 generally includesone or more processing units (CPUs) 402, one or more network or othercommunications interfaces 410, memory 412, and one or more communicationbuses 414 for interconnecting these components. The system 400 mayoptionally include a user interface 404, for instance a display 406 anda keyboard 408. Memory 412 may include high speed random access memoryand may also include non-volatile memory, such as one or more magneticdisk storage devices. Memory 412 may include mass storage that isremotely located from the central processing unit(s) 402.

The memory 412 stores an operating system 416 (e.g., Linux or Unix), anetwork communication module 418, a system initialization module 420,application software 422 and a library 430. The operating system 416generally includes procedures for handling various basic system servicesand for performing hardware dependent tasks. The network communicationmodule 418 is used for connecting the system 400 to a file system (FS)446, servers or other computing devices via one or more communicationnetworks, such as the Internet, other wide area networks, local areanetworks, metropolitan area networks, and the like. The systeminitialization module 420 initializes other modules and data structuresstored in memory 414 required for the appropriate operation of thesystem 400. In some embodiments, the application software 422 includes amap operator 424, a reduce operator 426 and a partition operator 428,and the library 430 includes application-independent map functions 432,reduce functions 434, and partition functions 436. As discussed above,the application software 422 may also include a combiner operator 425when the map-reduce operation meets certain conditions. The functions,procedures or instructions in the library 430 handle the applicationindependent aspects of large scaled data processing jobs, while theapplication software 422 provides the application-specific functions forproducing output data. The application software 422 may include sourceprograms for the map, combiner, reduce and partition operators as wellas the corresponding compiled programs, represented by binary files 212and 312 in FIGS. 2 and 3, respectively.

One or more status tables 444 are also included to track tasks andprocesses, as described with respect to FIGS. 7A and 7B. In someembodiments, the computer system 400 includes worker processes 438,intermediate files 440, and one or more master process(es) 442. Theinteraction of worker processes 438 and master processes 442 weredescribed with respect to FIG. 3.

Referring to FIGS. 2, 3 and 4, an application programmer can create ascript or program using the application software 422, which includes oneor more operators 424, 426 and 428. The script or program is processedinto binary files 212, 312 and provided to the work queue master 214,314.

For the embodiment shown in FIG. 2, input files 202 are split intomultiple data blocks and assigned by the work queue master 214 toindividual, application independent map and reduce processes 204 and208. The processes 204 invoke map functions 432 to process the inputdata (e.g., counting the number of occurrences of a term) to provideintermediate data values. In some embodiments, the input data isstructured in the form of key-value pairs. The partition function 436partitions the map output into one or more intermediate files 440, whichare stored on the FS 446. The intermediate data values are processed bythe map and reduce processes 204 and 208, which invoke reduce functions208 for sorting and combining intermediate data values having the samekey, and for storing the key and values in one or more output files 210located on the FS 446. The work queue master 214 manages the map andreduce processes 204 and 208 with the assistance of status tables 444,as described with respect to FIGS. 7A and 7B.

For the embodiment shown in FIG. 3, input files 302 are split intomultiple data blocks and assigned by the master process 442 toindividual, application independent worker processes 438. The workerprocesses 438 invoke map functions 432 for operating on blocks of inputdata (e.g., counting the number of occurrences of a term) to provideintermediate data values. The partition function 436 partitions the mapoutput into one or more intermediate files 440, which are stored locallyin memory 412. The intermediate data values are processed by applicationindependent worker processes 438, which invoke reduce functions 434 forsorting and combining intermediate data values having the same key, andfor storing the resulting output data in one or more output files 310located on the file system 446. The master process 442 manages theworker processes 436 with the assistance of status tables 444, asdescribed with respect to FIGS. 7A and 7B.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method of performing large-scale processing of data in adistributed and parallel processing environment, comprising: at a set ofinterconnected computing systems, each having one or more processors andmemory: identifying an application-specific map operation to retrievedata and produce intermediate data values; identifying anapplication-specific reduce operation to combine the intermediate datavalues; executing a plurality of map worker processes, wherein each mapworker process executes the map operation to read designated portions ofinput files, produce the intermediate data values in accordance with themap operation, and store the intermediate data values in intermediatedata structures; executing a plurality of reduce worker processes,wherein each reduce worker process executes the reduce operation to reada respective subset of the intermediate data values from theintermediate data structures and to produce final output data bycombining the respective subset of the intermediate data values inaccordance with the reduce operation; and tracking a status of aplurality of tasks executed by the map worker processes and the reduceworker processes.
 2. The method of claim 1, wherein tracking the statusof the plurality of tasks comprises storing the status in one or moretables.
 3. The method of claim 1, further comprising: determining theplurality of tasks associated with the map operation and the reduceoperation; and assigning the plurality of tasks to the plurality of mapworker processes and the plurality of reduce worker processes; wherein:determining the plurality of tasks comprises determining, for the inputfiles, a plurality of map tasks specifying data from the input files tobe processed into the intermediate data values and a plurality of reducetasks, each specifying a respective subset of the intermediate datavalues to be processed into the final output data; assigning the maptasks comprises assigning the map tasks to underutilized ones of the mapworker processes; and assigning the reduce tasks comprises assigning thereduce tasks to underutilized ones of the reduce worker processes. 4.The method of claim 3, wherein: the set of interconnected computersystems are grouped into a plurality of datacenters; and assigning themap tasks to underutilized ones of the map worker processes comprisesassigning map tasks for data stored on computer systems in a respectivedatacenter to map worker processes that are running on computer systemsin the respective datacenter.
 5. The method of claim 1, furthercomprising applying a partition operation to the intermediate datavalues, wherein the partition operation specifies a respectiveintermediate data structure of the set of intermediate data structuresin which to store each intermediate data value.
 6. The method of claim1, further comprising: identifying an application-specific combineroperation, distinct from the application-specific map operation and theapplication-specific reduce operation, for combining initial data valuesproduced by the application-specific map operation so as to produce theintermediate data values.
 7. The method of claim 6, wherein executingthe plurality of map worker processes includes executing the mapoperation to read designated portions of input files and produce theinitial data values and executing the combiner operation to combine theinitial data to produce the intermediate data values and store theintermediate data values in intermediate data structures.
 8. The methodof claim 1, wherein the intermediate data values comprise key-valuepairs, and identifying the reduce operation is in addition to theidentifying the map operation.
 9. The method of claim 8, wherein thereduce operation combines key-value pairs having a same key, and whereincombining key-value pairs having a same key comprises, for each distinctkey, forming a respective aggregated key-value pair whose key is therespective key and whose value is a sum of the values of the key-valuepairs whose keys match the respective key.
 10. A system for large-scaleprocessing of data in a distributed and parallel processing environment,comprising: a set of interconnected computing systems, each having oneor more processors and memory, the set of interconnected computingsystems including: an application-specific map operation; anapplication-specific reduce operation to combine the intermediate datavalues in accordance with the reduce operation; a plurality of mapworker processes, wherein each map worker process executes theapplication-specific map operation to read designated portions of inputfiles, produce the intermediate values in accordance with theapplication-specific map operation, and store the intermediate datavalues in intermediate data structures; a plurality of reduce workerprocesses, wherein each reduce worker process executes theapplication-specific reduce operation to read a respective subset of theintermediate data values from the intermediate data structures and toproduce final output data by combining the respective subset of theintermediate data values in accordance with the application-specificreduce operation; and a tracking operation that tracks a status of aplurality of tasks executed by the map worker processes and the reduceworker processes.
 11. The system of claim 10, further comprising onemore tables, wherein the one are more tables are configured for thetracking of the status of the plurality of tasks.
 12. The system ofclaim 10, further comprising: an operation that determines the pluralityof tasks associated with the map operation and the reduce operation; andan assigning operation that assigns the plurality of tasks to theplurality of map worker processes and the plurality of reduce workerprocesses; wherein determining the plurality of tasks comprisesdetermining, for the input files, a plurality of map tasks specifyingdata from the input files to be processed into the intermediate datavalues and a plurality of reduce tasks, each specifying a respectivesubset of the intermediate data values to be processed into the finaloutput data; and wherein assigning the plurality of tasks comprisesassigning the map tasks to underutilized ones of the map workerprocesses, and assigning the reduce tasks to underutilized ones of thereduce worker processes.
 13. The system of claim 12, wherein: the set ofinterconnected computer systems are grouped into a plurality ofdatacenters; and when assigning the map tasks to underutilized ones ofthe map worker processes, the supervisory process preferentially assignsmap tasks for data stored on computer systems in a respective datacenterto map worker processes that are running on computer systems in therespective datacenter.
 14. The system of claim 10, further comprising apartition operation that operates on intermediate data values, whereinthe partition operation specifies a respective intermediate datastructure of the set of intermediate data structures in which to storeeach intermediate data value.
 15. The system of claim 14, wherein theintermediate data values comprise key-value pairs, and the reduceoperation is in addition to the map operation.
 16. The system of claim15, wherein the reduce operation combines key-value pairs having a samekey, wherein combining key-value pairs having a same key comprises, foreach distinct key, forming a respective aggregated key-value pair whosekey is the respective key and whose value is a sum of the values of thekey-value pairs whose keys match the respective key.
 17. Anon-transitory computer readable storage medium storing one or moreprograms configured for execution by a plurality of processors of a setof interconnected computing systems, the one or more programs comprisinginstructions for: identifying an application-specific map operation toretrieve data and produce intermediate data values in accordance withthe operation; identifying an application-specific reduce operation tocombine the intermediate data values in accordance with the reduceoperation; executing a plurality of map worker processes, wherein eachmap worker process executes the map operation to read designatedportions of input files, produce the intermediate data values inaccordance with the map operation, and store the intermediate datavalues in intermediate data structures; executing a plurality of reduceworker processes, wherein each reduce worker process executes the reduceoperation to read a respective subset of the intermediate data valuesfrom the intermediate data structures and to produce final output databy combining the respective subset of the intermediate data values inaccordance with the reduce operation; and tracking a status of aplurality of tasks executed by the map worker processes and the reduceworker processes.
 18. The non-transitory computer readable storagemedium of claim 17, wherein tracking the status of the plurality oftasks comprises storing the status in one or more tables.
 19. Thenon-transitory computer readable storage medium of claim 17, furthercomprising: determining the plurality of tasks associated with the mapoperation and the reduce operation; and assigning the plurality of tasksto the plurality of map worker processes and the plurality of reduceworker processes.
 20. The non-transitory computer readable storagemedium of claim 19, wherein: determining the plurality of taskscomprises determining, for the input files, a plurality of map tasksspecifying data from the input files to be processed into theintermediate data values and a plurality of reduce tasks, eachspecifying a respective subset of the intermediate data values to beprocessed into the final output data; assigning the map tasks comprisesassigning the map tasks to underutilized ones of the map workerprocesses; and assigning the reduce tasks comprises assigning the reducetasks to underutilized ones of the reduce worker processes.